Super-resolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. SIM consists of two generic components: (i) sample illumination by a sinusoidal pattern and (ii) computational reconstruction of a super-resolution image. The rate-limiting step in observing a super-resolution image in SIM is the reconstruction speed of the algorithm required to form a single image from as many as nine raw images. These reconstruction algorithms impose a significant computing burden due to a complex workflow and a large number of calculations requiring 10-300 seconds per image nullifying real-time imaging. In this mini-review, we show how the approaches we developed to improve Hessian-SIM algorithm reconstruction speed can be used to improve other SIM image reconstruction algorithms. These approaches, which included code improvement, conversion to the GPU environment, and use of cost-effective high-performance computers produce up to 500-fold increases in image reconstruction speed.
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